An intrusion detection system for denial of service attack detection in internet of things

2017 ◽  
Author(s):  
Breno Fabrício Lira Melo Sousa ◽  
Zair Abdelouahab ◽  
Denivaldo Cicero Pavão Lopes ◽  
Natalia Costa Soeiro ◽  
Willian França Ribeiro
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Denial Of Service Attack ◽  
Service Attack

Multilevel Intrusion Detection System to Mitigate Denial-of-Service Attack in Wireless Sensor Networks

2020 ◽  
Vol 17 (6) ◽  
pp. 2451-2458
Author(s):  
Shilpy Ghai ◽  
Vijay Kumar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Data Encryption ◽  
Denial Of Service Attack ◽  
Malicious Behavior ◽  
Result Show ◽  
Service Attack ◽  
Open Issue

Malicious activities over WSN is quite hard to detect as sensors operate in an open network environment. Researchers have offered several solutions but still intrusion detection/prevention is an open issue. In this paper, a scheme is introduced that can analyze the malicious behavior of the nodes over multiple layers. It uses AES algorithm for data encryption and its integrity is insured using SHA512 method. Simulation results show that it outperforms as compared to traditional WatchDog method under QoS constraints. Simulation result show that it outperforms as compared to traditional watchdog scheme.

Machine Learning Based Intrusion Detection System for Denial of Service Attack

2021 ◽  
pp. 29-47
Author(s):  
Ashish Pandey ◽  
Neelendra Badal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dos Attack ◽  
Denial Of Service Attack ◽  
Service Attack

Machine learning-based intrusion detection system (IDS) is a research field of network security which depends on the effective and accurate training of models. The models of IDS must be trained with new attacks periodically; therefore, it can detect any security violations in the network. One of most frequent security violations that occurs in the network is denial of service (DoS) attack. Therefore, training of IDS models with latest DoS attack instances is required. The training of IDS models can be more effective when it is performed with the help of machine learning algorithms because the processing capabilities of machine learning algorithms are very fast. Therefore, the work presented in this chapter focuses on building a model of machine learning-based intrusion detection system for denial of service attack. Building a model of IDS requires sample dataset and tools. The sample dataset which is used in this research is NSL-KDD, while WEKA is used as a tool to perform all the experiments.

Application of Deep Learning Technique in an Intrusion Detection System

2020 ◽  
Vol 19 (02) ◽  
pp. 2050016
Author(s):  
Shideh Saraeian ◽  
Mahya Mohammadi Golchi
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Visual Analysis ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Hybrid Network ◽  
Machine Learning Techniques ◽  
Learning Technique

Comprehensive development of computer networks causes the increment of Distributed Denial of Service (DDoS) attacks. These types of attacks can easily restrict communication and computing. Among all the previous researches, the accuracy of the attack detection has not been properly addressed. In this study, deep learning technique is used in a hybrid network-based Intrusion Detection System (IDS) to detect intrusion on network. The performance of the proposed technique is evaluated on the NSL-KDD and ISCXIDS 2012 datasets. We performed traffic visual analysis using Wireshark tool and did some experimentations to prove the superiority of the proposed method. The results have shown that our proposed method achieved higher accuracy in comparison with other useful machine learning techniques.

scholarly journals Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

2021 ◽  
Author(s):  
Kathiroli Raja ◽  
Krithika Karthikeyan ◽  
Abilash B ◽  
Kapal Dev ◽  
Gunasekaran Raja
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Production Management ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Sensitive Information ◽  
Smart Meters ◽  
Network Intrusion

Abstract The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified,
1
stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

scholarly journals Anomaly Detection in Distributed Denial of Service Attack using Map Reduce Improvised Counter Based Algorithm in Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 4668-4671
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Map Reduce ◽  
Distributed Denial Of Service ◽  
Unusual Pattern ◽  
Denial Of Service Attack

A Distributed denial of Service attacks(DDoS) is one of the major threats in the cyber network and it attacks the computers flooded with the Users Data Gram packet. These types of attacks causes major problem in the network in the form of crashing the system with large volume of traffic to attack the victim and make the victim idle in which not responding the requests. To detect this DDOS attack traditional intrusion detection system is not suitable to handle huge volume of data. Hadoop is a frame work which handles huge volume of data and is used to process the data to find any malicious activity in the data. In this research paper anomaly detection technique is implemented in Map Reduce Algorithm which detects the unusual pattern of data in the network traffic. To design a proposed model, Map Reduce platform is used to hold the improvised algorithm which detects the (DDoS) attacks by filtering and sorting the network traffic and detects the unusual pattern from the network. Improvised Map reduce algorithm is implemented with Map Reduce functionalities at the stage of verifying the network IPS. This Proposed algorithm focuses on the UDP flooding attack using Anomaly based Intrusion detection system technique which detects kind of pattern and flow of packets in the node is more than the threshold and also identifies the source code causing UDP Flood Attack.

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